Big Data & AI World London Day 2: From Curiosity to Measurable Capability

Day two of the Big Data & AI World Keynote Theatre examined how organisations translate AI experimentation into measurable performance. Where earlier discussions centred on trust, governance and leadership alignment, the focus shifted toward execution: how AI initiatives deliver tangible results inside live operating environments.

Across motorsport, travel and telecommunications, a consistent pattern emerged. AI maturity is less about access to models and more about organisational alignment, disciplined experimentation and cultural readiness.

Racing Into Intelligence: AI in Motorsport

Ben Buckley, Head of Product Engineering at Atlassian Williams F1 Team, offered a detailed view of AI deployment inside Formula 1.

The sport is already deeply data-driven. A single race weekend generates substantial telemetry.

“Overall… probably about a terabyte.”

Nearly all of that data becomes useful over time.

“Everything we collect will be used eventually.”

Yet competitive advantage does not come from accumulating more data alone. Cost caps and regulatory limits restrict brute-force expansion. Instead, the differentiator lies in workflow quality and decision optimisation.

On the evolution of AI within the team, Buckley explained:

“It will be the same sort of data backing it, but it’ll give them more flexibility.”

AI augments decision-making rather than replacing engineers. Human judgement remains central.

The most immediate gains have emerged inside engineering workflows. Legacy systems that once required six-month modernisation cycles are now rebuilt in significantly shorter windows.

“That would have previously been… going to take six months. That is a week or two.”

Looking ahead, Buckley identified opportunities to streamline complex cross-functional workflows, from car design through wind tunnel scheduling to factory manufacturing. At the same time, he cautioned against uncontrolled acceleration.

“The opportunities are huge, the risk is bigger.”

Both over-adoption and under-adoption carry risk. Managing pace and governance is critical.

“It’s really hard to see past the next six, 12 months of just how fast it’s moving.”

In Formula 1, milliseconds determine outcomes. In enterprise environments, the timeline may be longer, but the competitive pressure is comparable.

Making AI Stick: Scaling at Travelopia

Chris Storey of Travelopia addressed a challenge familiar to many enterprises: why promising AI pilots fail to scale.

“AI doesn’t fail because of the technology; it fails because scaling is difficult and managing people is difficult.”

Early chatbot initiatives generated enthusiasm, but sustained adoption required more than tooling.

Controlled experiments comparing developers with and without AI tools revealed an initial productivity gap.

“For about the first three or four weeks… the software engineers without AI were faster.”

Over time, as familiarity increased and workflows adjusted, the trend reversed. Capability deepened, and productivity improved. Today, AI integration is substantial.

“Around 80% of our code is AI-generated.”

Delivery timelines have also shortened.

“We’re talking days instead of weeks.”

However, Storey emphasised that technology alone does not ensure maturity.

“Licenses do not equal adoption…Curiosity doesn’t equal capability.”

Travelopia invested in leadership engagement, change management and structured frameworks that link AI initiatives directly to measurable business outcomes.

“Great… all the licenses, all the training. Wonderful. But so what?”

The objective is value creation, not activity metrics.

“AI won’t replace humans… The humans with AI will replace humans without AI.”

AI maturity, in this context, mirrors organisational maturity.

From Incubation to Impact: Virgin Media O2

Vanessa Barone, Head of Product – Ecommerce & AI, Virgin Media o2, explored transformation within a large-scale enterprise serving millions of customers.

AI success, she argued, depends on behavioural change as much as technical implementation.

“I massively underestimated the change curve.”

While some teams embraced experimentation, others responded with hesitation. To address this, Barone introduced a “playground environment” that allowed safe experimentation and shared learning.

“I spammed the entire team when I made some Outlook invites on Co-Pilot, and we just had a bit of a giggle around it.”

Normalising experimentation reduced resistance and encouraged adoption. Strategically, AI is embedded rather than peripheral.

“We’re not treating AI as though it’s a side dish. It’s part of the main meal.”

For Virgin Media O2, success combines business performance, customer value and long-term adaptability.

“The business, the customer and the future are always on the table.”

Barone also observed broader shifts.

“It’s not just our roles in the workplace that are changing, it’s our roles as humans that are changing.”

In highly competitive sectors, sustainable advantage depends on cultural resilience.

“Laser focus on the customer.”

And:

“Employee culture that embraces disruption.”

Measuring What Matters

Across elite sport, global travel and telecommunications, the discussions pointed toward a shared conclusion. AI initiatives are moving beyond experimentation, but outcomes depend on structured alignment rather than model sophistication alone.

Clear business objectives, disciplined experimentation frameworks, cultural readiness and measurable performance indicators emerged as consistent prerequisites. Leadership engagement and workforce adaptation proved as significant as tooling decisions.

As Moderator Ifeoluwa Ogunbufunmi stated at the outset:

“AI maturity is becoming a proxy for organisational maturity.”

Day two reinforced that perspective. Scaling AI effectively requires coordination across people, process and performance, with governance embedded from the beginning.

The conversation has progressed from exploration to execution. The challenge now lies in ensuring that curiosity evolves into capability, and that capability translates into sustained value.

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